High-fidelity Imaging Spectroscopy Research Articles

Alpha and beta diversity mapping in Indian tropical deciduous forests using high-fidelity imaging spectroscopy

The present study evaluates the potential of airborne hyperspectral and space-borne multispectral satellite datasets to determine alpha (α) and beta (β) diversity combining in-situ floristic composition in tropical deciduous forests (TDFs) of Mudumalai Tiger Reserve (MTR), India. 43 forest plots (each of 0.04 ha) were sampled to record the species diversity in MTR. The very high-resolution AVIRIS-NG hyperspectral images and temporal Sentinel-2 multispectral images were used to extract spectral variables by associating each field plot with corresponding pixels of the satellite images. A high positive correlation of field-based α-diversity (Shannon-Wiener diversity index (H′)) with AVIRIS-NG based α-diversity (R2: 0.83 (2016); R2: 0.82 (2018)) was observed compared to Sentinel-2 based α-diversity (R2: 0.50 to 0.63) with high correlation during post-monsoon (R2: 0.63). Principal coordinate analysis of AVIRIS-NG based β-diversity signifies a low diversity (H′: <1.00) in the eastern parts (0.13% of MTR) in contrast to moderate (H′: 1.00 to 2.00; 13.39% of MTR) to high diversity (H′ >2.00; 86.49% of MTR) in the remaining parts of MTR. The study highlights the application of high spectral-spatial AVIRIS-NG images for precise tree diversity mapping and the high temporal-spatial Sentinel-2 satellite images for assessing the phenological variations of the forest species. The integration of high spatial-hyperspectral and temporal multispectral satellite datasets with field-based observations for fine-scale tree species diversity mapping at geographical and temporal scales is the novelty of the study that may effectively contribute in devising effective forest management plans for biodiversity conservation.

High-Resolution Reef Bathymetry and Coral Habitat Complexity from Airborne Imaging Spectroscopy

Coral reef ecosystems are rapidly changing, and a persistent problem with monitoring changes in reef habitat complexity rests in the spatial resolution and repeatability of measurement techniques. We developed a new approach for high spatial resolution (&lt;1 m) mapping of nearshore bathymetry and three-dimensional habitat complexity (rugosity) using airborne high-fidelity imaging spectroscopy. Using this new method, we mapped coral reef habitat throughout two bays to a maximum depth of 25 m and compared the results to the laser-based SHOALS bathymetry standard. We also compared the results derived from imaging spectroscopy to a more conventional 4-band multispectral dataset. The spectroscopic approach yielded consistent results on repeat flights, despite variability in viewing and solar geometries and sea state conditions. We found that the spectroscopy-based results were comparable to those derived from SHOALS, and they were a major improvement over the multispectral approach. Yet, spectroscopy provided much finer spatial information than that which is available with SHOALS, which is valuable for analyzing changes in benthic composition at the scale of individual coral colonies. Monitoring temporal changes in reef 3D complexity at high spatial resolution will provide an improved means to assess the impacts of climate change and coastal processes that affect reef complexity.

What mediates tree mortality during drought in the southern Sierra Nevada?

Severe drought has the potential to cause selective mortality within a forest, thereby inducing shifts in forest species composition. The southern Sierra Nevada foothills and mountains of California have experienced extensive forest dieback due to drought stress and insect outbreak. We used high-fidelity imaging spectroscopy (HiFIS) and light detection and ranging (LiDAR) from the Carnegie Airborne Observatory (CAO) to estimate the effect of forest dieback on species composition in response to drought stress in Sequoia National Park. Our aims were (1) to quantify site-specific conditions that mediate tree mortality along an elevation gradient in the southern Sierra Nevada Mountains, (2) to assess where mortality events have a greater probability of occurring, and (3) to estimate which tree species have a greater likelihood of mortality along the elevation gradient. A series of statistical models were generated to classify species composition and identify tree mortality, and the influences of different environmental factors were spatially quantified and analyzed to assess where mortality events have a greater likelihood of occurring. A higher probability of mortality was observed in the lower portion of the elevation gradient, on southwest- and west-facing slopes, in areas with shallow soils, on shallower slopes, and at greater distances from water. All of these factors are related to site water balance throughout the landscape. Our results also suggest that mortality is species-specific along the elevation gradient, mainly affecting Pinus ponderosa and Pinus lambertiana at lower elevations. Selective mortality within the forest may drive long-term shifts in community composition along the elevation gradient.

Landscape-scale variation in canopy water content of giant sequoias during drought

Recent drought (2012–2016) caused unprecedented foliage dieback in giant sequoias (Sequoiadendron giganteum), a species endemic to the western slope of the southern Sierra Nevada in central California. As part of an effort to understand and map sequoia response to droughts, we studied the patterns of remotely sensed canopy water content (CWC), both within and among sequoia groves in two successive years during the drought period (2015 and 2016). Our aims were: (1) to quantify giant sequoia responses to severe drought stress at a landscape scale using CWC as an indicator of crown foliage status, and (2) to estimate the effect of environmental correlates that mediate CWC change within and among giant sequoia groves. We utilized airborne high fidelity imaging spectroscopy (HiFIS) and light detection and ranging (LiDAR) data from the Carnegie Airborne Observatory to assess giant sequoia foliage status during 2015 and 2016 of the 2012–2016 droughts. A series of statistical models were generated to classify giant sequoias and to map their location in Sequoia and Kings Canyon National Parks (SEKI) and vicinity. We explored the environmental correlates and the spatial patterns of CWC change at the landscape scale. The mapped CWC was highly variable throughout the landscape during the two observation years, and proved to be most closely related to geological substrates, topography, and site-specific water balance. While there was an overall net gain in sequoia CWC between 2015 and 2016, certain locations (lower elevations, steeper slopes, areas more distant from surface water sources, and areas with greater climate water deficit) showed CWC losses. In addition, we found greater CWC loss in shorter sequoias and those growing in areas with lower sequoia stem densities. Our results suggest that CWC change indicates sequoia response to droughts across landscapes. Long-term monitoring of giant sequoia CWC will likely be useful for modeling and predicting their population-level response to future climate change.

Liana canopy cover mapped throughout a tropical forest with high-fidelity imaging spectroscopy

Abstract Increasing size and abundance of lianas relative to trees are pervasive changes in Neotropical forests that may lead to reduced forest carbon stocks. Yet the liana growth form is chronically understudied in large-scale tropical forest censuses, resulting in few data on the scale, cause, and impact of increasing lianas. Satellite and airborne remote sensing provide potential tools to map and monitor lianas at much larger spatial and rapid temporal scales than are possible with plot-based forest censuses. We combined high-resolution airborne imaging spectroscopy and a ground-based tree canopy census to investigate whether tree canopies supporting lianas could be discriminated from tree canopies with no liana coverage. Using support vector machine algorithms, we achieved accuracies of nearly 90% in discriminating the presence–absence of lianas, and low error (15.7% RMSE) when predicting liana percent canopy cover. When applied to the full image of the study site, our model had a 4.1% false-positive error rate as validated against an independent plot-level dataset of liana canopy cover. Using the derived liana cover classification map, we show that 6.1%–10.2% of the 1823 ha study site has high-to-severe (50–100%) liana canopy cover. Given that levels of liana infestation are increasing in Neotropical forests and can result in high tree mortality, the extent of high-to-severe liana canopy cover across the landscape may have broad implications for ecosystem function and forest carbon storage. The ability to accurately map landscape-scale liana infestation is crucial to quantifying their effects on forest function and uncovering the mechanisms underlying their increase.

Organismic-Scale Remote Sensing of Canopy Foliar Traits in Lowland Tropical Forests

Airborne high fidelity imaging spectroscopy (HiFIS) holds great promise for bridging the gap between field studies of functional diversity, which are spatially limited, and satellite detection of ecosystem properties, which lacks resolution to understand within landscape dynamics. We use Carnegie Airborne Observatory HiFIS data combined with field collected foliar trait data to develop quantitative prediction models of foliar traits at the tree-crown level across over 1000 ha of humid tropical forest. We predicted foliar leaf mass per area (LMA) as well as foliar concentrations of nitrogen, phosphorus, calcium, magnesium and potassium for canopy emergent trees (R2: 0.45–0.67, relative RMSE: 11%–14%). Correlations between remotely sensed model coefficients for these foliar traits are similar to those found in laboratory studies, suggesting that the detection of these mineral nutrients is possible through their biochemical stoichiometry. Maps derived from HiFIS provide quantitative foliar trait information across a tropical forest landscape at fine spatial resolution, and along environmental gradients. Multi-nutrient maps implemented at the fine organismic scale will subsequently provide new insight to the functional biogeography and biological diversity of tropical forest ecosystems.

Mapping tropical forest canopy diversity using high‐fidelity imaging spectroscopy.

There is a growing need for operational biodiversity mapping methods to quantify and to assess the impact of climate change, habitat alteration, and human activity on ecosystem composition and function. Here, we present an original method for the estimation of α- and β-diversity of tropical forests based on high-fidelity imaging spectroscopy. We acquired imagery over high-diversity Amazonian tropical forest landscapes in Peru with the Carnegie Airborne Observatory and developed an unsupervised method to estimate the Shannon index (H′) and variations in species composition using Bray-Curtis dissimilarity (BC) and nonmetric multidimensional scaling (NMDS). An extensive field plot network was used for the validation of remotely sensed α- and β-diversity. Airborne maps of H′ were highly correlated with field α-diversity estimates (r = 0.86), and BC was estimated with demonstrable accuracy (r = 0.61–0.76). Our findings are the first direct and spatially explicit remotely sensed estimates of α- and β-diversity of humid tropical forests, paving the way for new applications using airborne and space-based imaging spectroscopy.

Tree Species Discrimination in Tropical Forests Using Airborne Imaging Spectroscopy

We identify canopy species in a Hawaiian tropical forest using supervised classification applied to airborne hyperspectral imagery acquired with the Carnegie Airborne Observatory-Alpha system. Nonparametric methods (linear and radial basis function support vector machine, artificial neural network, and k-nearest neighbor) and parametric methods (linear, quadratic, and regularized discriminant analysis) are compared for a range of species richness values and training sample sizes. We find a clear advantage in using regularized discriminant analysis, linear discriminant analysis, and support vector machines. No unique optimal classifier was found for all conditions tested, but we highlight the possibility of improving support vector machine classification with a better optimization of its free parameters. We also confirm that a combination of spectral and spatial information increases accuracy of species classification: we combine segmentation and species classification from regularized discriminant analysis to produce a map of the 17 discriminated species. Finally, we compare different methods to assess spectral separability and find a better ability of Bhattacharyya distance to assess separability within and among species. The results indicate that species mapping is tractable in tropical forests when using high-fidelity imaging spectroscopy.

Top-Down Analysis of Forest Structure and Biogeochemistry Across Hawaiian Landscapes

Technical and analytical improvements in aircraft-based remote sensing allow synoptic measurements of structural and chemical properties of vegetation across whole landscapes. We used the Carnegie Airborne Observatory, which includes waveform light detection and ranging (LiDAR) and high-fidelity imaging spectroscopy, to evaluate the landscapes surrounding four well-studied sites on a substrate age gradient across the Hawaiian Islands. The airborne measurements yielded variations in ground topography, canopy height, and canopy nitrogen (N) concentration more accurately than they could have been obtained by any reasonable intensity of ground-based sampling. We detected spatial variation in ecosystem properties associated with the properties of different species, including differences in canopy N concentrations associated with the native species Metrosideros polymorpha and Acacia koa, and differences brought about by invasions of the biological N fixer Morella faya. Structural and chemical differences associated with exotic tree plantations and with dominance of forest patches by the native mat-forming fern Dicranopteris linearis also could be analyzed straightforwardly. This approach provides a powerful tool for ecologists seeking to expand from plot-based measurements to landscape-level analyses.

DESERTIFICATION IN CENTRAL ARGENTINA: CHANGES IN ECOSYSTEM CARBON AND NITROGEN FROM IMAGING SPECTROSCOPY

Many arid and semiarid (dryland) regions are subject to desertification from intensive land-use pressures such as cattle ranching. However, the lack of quantitative approaches required to assess desertification has slowed our understanding of how vegetation and soils are changing in dryland regions. Using airborne high-fidelity imaging spectroscopy and field measurements, we developed the first regional assessment of vegetation structural and soil biogeochemical properties in the Monte Desert biome, Argentina. We evaluated the long-term impacts of grazing on vegetation cover and soil carbon (C) and nitrogen (N) storage, which are core indicators of biogeochemical status and change in drylands. A comparative analysis was carried out on vegetation and soil properties in four major Monte Desert plant communities, as well as within the U.N. Nacunan Man-and-Biosphere Reserve and adjacent areas subjected to long-term grazing. The four dominant plant communities differed substantially in vegetation cover, leaf area index, foliar N concentration, and soil organic C and N stocks. Imaging spectroscopy with Monte Carlo spectral mixture analysis provided accurate estimates of fractional photosynthetic vegetation (PV), nonphotosynthetic vegetation (NPV), and bare soil cover at <5-m spatial resolution throughout a 763-km2 region. Hotspots of grazing management (e.g., ranch centers, water sources) have undergone some woody vegetation encroachment, but the spatial patterns were localized and variable. More clearly, a widespread NPV decrease and bare soil increase was common outside of the reserve. Soil organic C and N stocks were highly correlated with PV + NPV cover fractions. Soil organic C and N storage was 25–80% lower in areas subjected to long-term grazing, as compared to protected ecosystems within the Nacunan Reserve. Long-term grazing has depleted stores of C and N in soils and dramatically altered vegetation structure in the Monte Desert. A persistent lack of NPV due to grazing has likely impaired C and N cycles considered central to the biogeochemical functioning of the region. This study demonstrates a novel approach to remotely measure both surface and soil properties most indicative of progressive land degradation and desertification in dryland regions. Corresponding Editor: D. Peters